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On the role of partial least squares in path analysis for the social sciences

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  • Dennis Cook, R.
  • Forzani, Liliana

Abstract

We describe the current and potential future roles for partial least squares (PLS) algorithms in path analyses, guided by recent advances in envelope theory. After reviewing the present debate and establishing a context, we conclude that, depending on specific objectives, PLS methods have considerable promise, but that their full potential, while reachable, is not now being realized. The future developments necessary for achieving their full potential in the social sciences are clear and doable, albeit demanding. A critique of covariance-based structural equation modeling (CB-SEM), as it relates to PLS, is given as well. Technical details are available in the appendix.

Suggested Citation

  • Dennis Cook, R. & Forzani, Liliana, 2023. "On the role of partial least squares in path analysis for the social sciences," Journal of Business Research, Elsevier, vol. 167(C).
    • Handle: RePEc:eee:jbrese:v:167:y:2023:i:c:s0148296323004915
    DOI: 10.1016/j.jbusres.2023.114132
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    References listed on IDEAS

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    1. Dijkstra, Theo, 1983. "Some comments on maximum likelihood and partial least squares methods," Journal of Econometrics, Elsevier, vol. 22(1-2), pages 67-90.
    2. Z. Su & G. Zhu & X. Chen & Y. Yang, 2016. "Sparse envelope model: efficient estimation and response variable selection in multivariate linear regression," Biometrika, Biometrika Trust, vol. 103(3), pages 579-593.
    3. R. Dennis Cook & Liliana Forzani & Xin Zhang, 2015. "Envelopes and reduced-rank regression," Biometrika, Biometrika Trust, vol. 102(2), pages 439-456.
    4. Yue Zhao & Ingrid Van Keilegom & Shanshan Ding, 2022. "Envelopes for censored quantile regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1562-1585, December.
    5. Peter Schönemann & Ming-Mei Wang, 1972. "Some new results on factor indeterminacy," Psychometrika, Springer;The Psychometric Society, vol. 37(1), pages 61-91, March.
    6. R. D. Cook & I. S. Helland & Z. Su, 2013. "Envelopes and partial least squares regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(5), pages 851-877, November.
    7. Sarstedt, Marko & Hair, Joseph F. & Ringle, Christian M. & Thiele, Kai O. & Gudergan, Siegfried P., 2016. "Estimation issues with PLS and CBSEM: Where the bias lies!," Journal of Business Research, Elsevier, vol. 69(10), pages 3998-4010.
    8. R. Dennis Cook & Bing Li & Francesca Chiaromonte, 2007. "Dimension reduction in regression without matrix inversion," Biometrika, Biometrika Trust, vol. 94(3), pages 569-584.
    9. Cook, R. Dennis & Forzani, Liliana & Liu, Lan, 2023. "Partial least squares for simultaneous reduction of response and predictor vectors in regression," Journal of Multivariate Analysis, Elsevier, vol. 196(C).
    10. R. Dennis Cook & Zhihua Su, 2013. "Scaled envelopes: scale-invariant and efficient estimation in multivariate linear regression," Biometrika, Biometrika Trust, vol. 100(4), pages 939-954.
    11. Rosseel, Yves, 2012. "lavaan: An R Package for Structural Equation Modeling," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i02).
    12. D. J. Hand, 1996. "Statistics and the Theory of Measurement," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(3), pages 445-473, May.
    13. Yeonhee Park & Zhihua Su & Hongtu Zhu, 2017. "Groupwise envelope models for imaging genetic analysis," Biometrics, The International Biometric Society, vol. 73(4), pages 1243-1253, December.
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